Role OverviewWe are seeking a Vice President of Embodied AI to lead the "brain" and "nervous system" of our humanoid robots. This role owns the company's AI strategy and the core AI and controls organization - real-time controls, whole-body reinforcement learning, dexterous manipulation, VLA post-training, and agentic autonomy - with a primary mandate to unify these stacks into a single, coherent system that performs reliable, useful work for our customers.
You will lead a world-class, multi-disciplinary organization of 45+ engineers and researchers. You will own the overarching technical AI strategy and data collection strategy, and serve as a crucial voice in executive discussions on the product and technology roadmap.
We are looking for a unique hybrid: a recognized thought leader in the embodied AI research community who possesses the hard-won pragmatism and engineering rigor required to ship physical products to commercial customers.
Key ResponsibilitiesAI Strategy & Technical Vision- Define and own the company's embodied AI vision and roadmap, aligned with product strategy and long-term autonomy goals.
- Drive technical decision-making around model architectures and the role of internal models versus strategic partner and third-party foundation and VLA models.
- Establish architectural principles for how learned and classical components compose into a safe, reliable robotic system.
- Partner with the CEO, CTO, and executive team to define the product and technology roadmap, translating complex AI capabilities into commercial milestones.
Controls Stack Unification- Architect and execute a unified controls strategy, bridging high-level semantic reasoning (VLAs and agentic planning), mid-level policy execution (RL), and low-level deterministic real-time control.
- Define clean interfaces, arbitration, and fallback behavior across the layers of the stack so that capability gains in one layer compound rather than conflict.
- Ensure the unified stack meets the latency, stability, and safety requirements of dynamic humanoid platforms operating around people.
Model Development & Autonomy- Lead development of the models and policies that power the robot: state estimation and real-time control, whole-body RL locomotion and coordination, dexterous high-DoF manipulation, VLA post-training and adaptation, and agentic task planning and error recovery.
- Establish best practices for fine-tuning, distillation and compression, safety constraints and guardrails, and continuous learning.
- Define the evaluation criteria and benchmarks that tie model performance to real-world robotic outcomes.
- Guide the adaptation of strategic partner and third-party foundation models into the production stack, prioritizing co-optimization where it drives performance, safety, and speed to deployment.
Data Collection & Learning Strategy- Own the end-to-end data strategy required to train state-of-the-art embodied AI: what data to collect, from which sources, at what scale, and to what quality bar.
- Define and drive data collection programs across teleoperation, real-world robot fleets, and synthetic data and simulation.
- Set dataset requirements, curation standards, and quality metrics for all training and evaluation data.
- Direct the design of simulation environments and scenarios used for training, evaluation, and sim-to-real transfer.
Customer-Centric Deployment- Ensure our controllers are reliable, safe, and deliver tangible ROI for customers.
- Own the definition of model readiness for deployment - the performance, safety, and robustness criteria a model must meet before reaching customer sites.
- Use field telemetry and deployment feedback to close the loop between real-world behavior, data collection priorities, and model improvement.
Thought Leadership & Recruiting- Act as an ambassador for the company within the global AI and robotics communities.
- Publicize key findings where aligned with IP strategy, and build strategic research and industry relationships.
- Attract, hire, and retain top-tier engineering and research talent.
Team & Organizational Leadership- Directly manage and scale a multi-disciplinary organization of engineers and researchers across real-time controls, whole-body RL control, dexterous hand control, VLA post-training, and autonomy frameworks.
- Collaborate with the software, infrastructure, and hardware organizations to ensure the AI stack is well supported from training through on-robot deployment.
- Set engineering and research standards, review practices, and career development paths.
- Foster a culture of technical excellence, experimentation, accountability, and cross-functional collaboration.
QualificationsRequired Experience- 10+ years in robotics, AI, or machine learning, with 5+ years in senior leadership (VP/Director) managing large, multi-disciplinary technical organizations (40+ engineers/researchers).
- Deep technical fluency across the modern robotics stack, including the trade-offs between classical control theory, reinforcement learning, and modern foundation models (VLAs, world action models, LLMs).
- Capable of taking complex, AI-driven hardware or robotics systems out of R&D and successfully deploying them to external customers in the real world - balancing "perfect" research with "good enough to ship."
- Experience defining and scaling the data strategy behind large-scale action models: teleoperation, real-world collection, auto-labeling, and sim-to-real transfer.
- Respected presence in the AI/robotics research community (e.g., publications at ICRA, IROS, CoRL, NeurIPS, CVPR), with a network that supports strategic hiring and partnerships.
- Exceptional communication: able to distill complex technical constraints into clear strategic decisions for the executive team, while diving deep into architecture discussions with staff engineers.
Strongly Preferred- Direct experience with humanoid or legged robotics, dexterous manipulation, or dynamic whole-body control.
- Experience integrating and co-optimizing with strategic partners or third-party foundation and VLA models.
- Experience navigating the safety and compliance challenges of deploying autonomous robots in human-centric environments.
- Advanced degree in Robotics, Computer Science, ML, or a related field.
What Success Looks Like- The controls and AI stacks operate as one unified architecture, from semantic reasoning down to real-time actuation.
- Robots reliably perform useful, revenue-generating work for customers, with AI capability translating directly into commercial milestones.
- The data collection strategy measurably accelerates model improvement across teleoperation, fleet, and simulation sources.
- Internal, partner, and third-party models are adopted pragmatically, based on what delivers performance, safety, and speed to deployment.
- The organization attracts world-class talent and is recognized as a leader in applied embodied AI, not just research.
- Executive and product decisions are grounded in a clear-eyed view of what the AI stack can deliver and when.
Physical Requirements- Prolonged periods of sitting at a desk and working on a computer
- Must be able to lift 15 pounds at times
- Vision to read printed materials and a computer screen
- Hearing and speech to communicate
*This is a direct hire. Please, no outside Agency solicitations.
